The detection of the presence, velocity and/or length of vehicles on roadways is increasingly recognized as critical for effective roadway congestion management and traffic safety. The use of vehicle presence detectors is common practice for traffic volume measurement, control of signalized intersections and on ramp meters. In addition, vehicle velocity and classification by length are important for automated incident detection and the characterization and prediction of traffic demand.
Real-time detection is also used for actuation of automated driver information systems, for example, the Caltrans Automated Warning System (CAWS) on I-5 in Central California. Among the commonly implemented sensing mechanisms used for vehicle detection are changes in inductance (loop detectors) or magnetic field strength (magnetometers), RADAR, optical and laser transmission or pulse time-of-flight, ultrasonic pulse return, electromagnetic signature and acoustic or signature discrimination. Detectors based on each of these methods are known to have advantages and limitations which make them appropriate for some implementations, but inappropriate for others.
Loop detectors and magnetometers must be installed in the pavement, and are therefore referred to as in-pavement detectors. Other above-mentioned detection methods require placement of the detectors above or to the side of traffic lanes, with each detector operative for one or more traffic lanes. The vehicle presence detectors associated with the various sensing methods have different detection characteristics and yield different results than inductive loop systems which may introduce uncertainties into traffic control systems if the detection characteristics are not determined.
To assess the performance of new or unproven vehicle presence detectors under varying real-world conditions, a common and objective set of standards data, generally referred to as “ground truth” data, is required. Obtaining ground truth data has traditionally been performed manually, relying on human observation, either directly at the roadway site or from the playback of videotapes.
Accordingly, manually evaluating the performance of new or unproven vehicle presence detectors is exceedingly time consuming, costly and prone to human error due to the tedium involved in comparing actual observations or video tape records to the results obtained from the detector undergoing testing. As such, a need exists in the relevant art to automate the collection of data from multiple detectors operative at the same location, the generation of the “ground truth” data set, the comparison of individual detector results with the ground truth data set, and the generation of accuracy statistics for each detector under test.